Spatial Load Forecast for Electric Vehicles Péter Kádár* and Rita Lovassy** *Inst. of Power Engineering Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest, Hungary **Inst. of Microelectronics and Technology Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest, Hungary e-mail: kadar.peter@ kvk.uni-obuda.hu,
[email protected]
Abstract — In this paper the Electric Vehicle’s (EV’s) spatial load forecast is studied, highlighting their current situation in Hungary. The impact of electric vehicles charging on electricity consumption, which generates a concentrated power demand at specific points of the network, is modeled. The extra power requires the development of network capacity, the extra energy requires more power generation. It is a new task to forecast and model the new demand in time and space for the network and generation planning.
I.
Our goal is to forecast the electric vehicles impact on the power grid in Hungary by 2020, to build up a methodology by studying their energy and power demand, furthermore to customize their charging schedules. The outline of the paper is as follows. After the Introduction, Section II presents the situation of electric vehicles in Hungary. In Section III examples are given to study the e-car usage habits, concluding their charging needs. Section IV models the cumulative load in time and space followed by Conclusions and References.
INTRODUCTION
In the past few years the Original Equipment Manufacturers (OEM) have more or less all started production of electric vehicles (EV) for concepts or to put them on the market [1]. BMW started in 2009 with an electric version of the Mini, available for 6-month rentals in different locations around the world [2]. Findings from the MINI E and the BMW ActiveE field trials will be integrated into the launch of the BMW i3 and BMW i8 plug-in hybrids under the new sub-brand BMW i, in late 2013. Most popular models were the Peugeot iON, Citroen C-Zero and Mitsubishi MiEV [3] Renault-Nissan Alliance was the first to launch a full electric car for the mass market with the Nissan Leaf [2]. Chevrolet's awardwinning Volt is the 2012 European "Car of the Year", driving up to 80 kilometers on electricity stored in the 16 kWh lithium-ion battery and emitting zero CO2 [4].
II.
ELECTRIC VEHICLES IN HUNGARY
Spatial load forecast for EV in Hungary is very important because in this way is predictable the country future electricity demand and the network development needs. In order to create a scenario we study what will be, in some main cities, the magnitude of total number of ecars by categories, what are their usage pattern, load power, and temporal characteristics. In the next years we suppose that the EVs will be used by large companies (especially in delivery, mass transportation and taxi), and individual users (citizens, small enterprises and car sharing). The medium term forecast for Hungary by 2020 estimates 1% EV from the total vehicles number (see Figure 1.).
The Chevrolet Volt is the first electric car with no range limitations. When the battery's energy is depleted after a maximum of 80 kilometers, a 1.4L petrol motor seamlessly kicks in and generates electricity to power the electric drive unit. This extends the range of the Volt to over 500 kilometers. To recharge, the Volt can be plugged into any 230V household outlet [4]. In the Western European region 7000 cars were sold in the first 4 months of 2012, which is significant growth compared to 2011. Almost in the same period, in the first five month of 2012, more than 14 thousand e-cars were sold in USA. China market goal is 1 million electric passenger cars by 2015 [3]. The major factors which influence the spread of electric vehicles in Hungary are the development of the charging infrastructure, the charging speed, the EV and batteries price, and not at least the driving range.
Figure 1.
Estimation of the total number of EVs by categories
Unfortunately, at the moment, the relatively low number of electric cars depends on the EV’s high price, the high price of batteries, the limited driving range and
the development of the charging furthermore on the charging speed [3].
infrastructure,
Figure 2 shows the estimation of spatial distribution of EVs in the main Hungarian cities in 2017. In the future, as it can be seen from our observations, a huge number of EVs are expected to be only in specific regions, especially in the metropolitan area of Budapest and in some main cities. These cars are fuelled (charged) also in these surroundings (at fuel stations, at home or at work). Basically we count with a slow charging method which requires more time (mainly by night) and in this way is encouraged the consumption of low cost night energy. This approach is beneficial for balancing the daily load curves with a “night valley” loading (DSM – Demand Side Management).
Figure3.
Typical car user habits
From the defined typical car usage patterns we concluded the charging needs. In the above example we count by cars with 0,2 kWh/km consumption, 5 or 10 kW, maximum 6 hour charging power and 50 kWh battery capacity. The storage efficiency is 80%. Figure 4 shows the battery status during the long term car usage. Figure 2.
Spatial distributions of EVs in Hungary
III.
USER’S BEHAVIOR
In this section we study the actual Hungarian car user’s behaviors, and we predict that this tendency will be similar in the next years. A. Traffic analyses In order to predict the impact of EVs charging on energy industry we study the typical car use-cases. The EV energy and power demand forecast depends on the vehicles numbers, their spatial distribution, from the car types, individual usage categories, number of travelled kilometers, daily traffic routines, and not least from the season. From an energy industry perspective is very important to know the power capacity required for the charging of batteries, and to ensure sufficient power transmission [3]. The next figures (see Figure 3) illustrate three examples of typical e-car usage habits, patterns. The data were collected from three family’s weekly routines. Car 1 used mainly at weekends, Car 2 used for daily work, and Car 3 used every day with some long journeys.
Figure 4. Car battery status
IV.
THE CUMULATIVE CONSUMPTION
In this section we model typical EVs (lorry, taxi, and car), typical usage habits (workdays, weekend, longrange) and the related charging needs. Also we forecast the number of the EVs available, for example taxi, in 2017, in a defined region (see Table I.).
TABLE I. SUPPOSED E-TAXIS NUMBER IN 2012 year
area
EV type
2017
Budapest
taxi
usage type All day
number of EVs 210
We suppose that the real power demand peak will be less than a simple sum of the partial loads because of the time shift of the beginning of the individual charging.
Figure 6.
Figure 5.
Loads of individual chargers
Load as a function of time is important. In our approach the individual peak loads which contributed to the system load are non-coincident, they do not appear in the same time and they are time shifted. The model has no problem tracking and is able to anticipate future changes in peak due to continued shifts. Figure 6 shows the main steps in the proposed temporal modeling, illustrating how depends the load curve shapes from the number of chargers turned on and off, furthermore from the number of chargers in operation, respectively.
Temporal modeling
In this approach the model integrates the area under the load curve during each period and stores this area, even if the present load fluctuates. The total power used is equal to difference between the number of chargers turned on and off times the mean value of the load power. The three chargers are supposed to be in Budapest area, during spring time. The characteristics highlight an individual charger’s contribution to system load, presenting the relation between power (in kW) and a whole week time period, the sampling period in the range of 1 hour. From the collected dates we concluded that the daily load behavior for an individual car is dominated by high peaks. Over a 24 hour period we detected one or two maximums at different times of the day. They were short, and did not appear in the same time. Utilities often represent individual load curves of consumers with smooth load curves [5]. In the next a normal distribution is used to characterize the uncertainty of loads [6]. In our approach we model the normal distribution using 7 discrete intervals having in this way a sufficient accurate model (see Figure 7). The intervals are represented by its midpoints. We choose for all original load levels the same standard deviation value.
-3σ -2σ –σ mean σ 2σ 3σ Figure 7.
Seven interval representation of normal distribution
In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has a bell-shaped probability density function [7].
P ( x) =
σ 2π
e
( )
−( x − µ ) / 2σ 2 2
1
shows how we model the maximum of the EV generated extra supply needs in 2017.
(1)
The parameter µ is the mean or expectation and σ2 is the variance. σ is known as the standard deviation. Based on the normal distribution, with a fixed standard deviation, in the next we model three charger types (considering 100-100 pieces from each). Based on our observations, in general, the chargers are used during the night, rarely during the day. Figure 9 (a-c) shows the chargers power demand in kW, during a week. The sampling time is 1 hour. By adding the characteristics, see Figure 9 (d), we obtained a typical system load on an average spring week. Based on our model we conclude that in this case the maximum value is around 1500 kW. Combining the local EVs charging needs we illustrate the local power need curves. For obtaining spatial load forecast this curves are simulated for each area of Hungary and are visualized parallel in the 3D bar diagram. An animation is also done based on the sequence of the hourly diagrams. Figure 8
Figure 8. EV generated extra supply need
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Figure 9 d. Typical system load during a week
V.
CONCLUSIONS
The Electric Vehicles charging generates a concentrated power demand at specific points of the network, having their specific load schedule. This demand produces high peaks in load curves. The extra power requires the development of the network capacity, the extra energy requires more power generation. In this paper we forecast and model the new demand in time and space for the network planning. The EVs makes considerable energy consumption. The probabilistic knowledge about the charging habits helps to plan the network development. The EV chargers can be a good tool for the Demand Side Management (DSM). In the future we intend to work with a more precise and full database, to fulfill the simulations in time and space, considering an exponential falloff of the curves.
ACKNOWLEDGMENT Many thanks to László Gál in modeling, and to students Gábor Szijártó, Engelbert Ruff and Gábor Szigethy for the data preparation.
REFERENCES [1] [2] [3] [4] [5] [6] [7]
http://www.cars21.com, 07th August 2012 ECG News, ECG – The Association of European Vehicle Logistics, Issue 12.31, 06th-10th August 2012 http://www.pwc.com/hu/en/kiadvanyok/look-into-the-future-of-ecars.jhtml media.chevroleteurope.com, www.chevroleteurope.com, July 4, 2012 H. Lee Willis: “Spatial Electric Load Forecasting”, Marcel Dekker Inc., 2002. W. Li: “Risk Assessment of Power Systems - Models, Methods, and Applications” Piscataway, NJ: IEEE Press/Wiley, 2005. http://en.wikipedia.org/wiki/Normal_distribution
